class_docstr: Defines a configuration class from which a TD3 Algorithm can be built.
  
  Example:
      >>> from ray.rllib.algorithms.td3 import TD3Config
      >>> config = TD3Config().training(lr=0.01).resources(num_gpus=1)
      >>> print(config.to_dict())  # doctest: +SKIP
      >>> # Build a Algorithm object from the config and run one training iteration.
      >>> algo = config.build(env="Pendulum-v1")  # doctest: +SKIP
      >>> algo.train()  # doctest: +SKIP
  
  Example:
      >>> from ray.rllib.algorithms.td3 import TD3Config
      >>> from ray import air
      >>> from ray import tune
      >>> config = TD3Config()
      >>> # Print out some default values.
      >>> print(config.lr)   # doctest: +SKIP
      >>> # Update the config object.
      >>> config = config.training(lr=tune.grid_search(  # doctest: +SKIP
      ...     [0.001, 0.0001]))  # doctest: +SKIP
      >>> # Set the config object's env.
      >>> config.environment(env="Pendulum-v1")  # doctest: +SKIP
      >>> # Use to_dict() to get the old-style python config dict
      >>> # when running with tune.
      >>> tune.Tuner(  # doctest: +SKIP
      ...     "TD3",
      ...     run_config=air.RunConfig(stop={"episode_reward_mean": 200}),
      ...     param_space=config.to_dict(),
      ... ).fit()
docstr: Initializes a TD3Config instance.
docstr_tok: ["Initializes","a","TD3Config","instance","."]
file_path: ray/rllib_contrib/td3/src/rllib_td3/td3/td3.py
imports: from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.ddpg.ddpg import DDPG, DDPGConfig
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
label_desc: Use image node_id 1 to create a new TD3Config object from inherited base classes: DDPGConfig with example: obj = TD3Config(algo_class)